Datasets:
TMarcus
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InternVL

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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.2868e-10, 6.1396e-11, 1.7115e-10, 9.9346e-11, 4.1612e-09,
5.2633e-09, 2.9391e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0115e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
ANSWER0=VQA(image=RIGHT,question='Do two parrots nuzzle in the image?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 1.8258e-10, 4.5894e-11, 1.1135e-10, 6.4452e-11, 3.4489e-09,
2.4862e-09, 2.7154e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.8258e-10, 4.5894e-11, 1.1135e-10, 6.4452e-11, 3.4489e-09,
2.4862e-09, 2.7154e-11], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(6.3666e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
question: ['Do two parrots nuzzle in the image?'], responses:['yes']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 6.6052e-09, 6.0236e-08, 2.1438e-09, 2.6009e-11, 4.2778e-11,
8.5876e-12, 5.4921e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.6052e-09, 6.0236e-08, 2.1438e-09, 2.6009e-11, 4.2778e-11,
8.5876e-12, 5.4921e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(6.0236e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.8974e-08, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 2.6245e-08, 5.4500e-11, 1.4481e-07, 3.4527e-11, 2.1388e-10,
4.9739e-10, 1.9941e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.6245e-08, 5.4500e-11, 1.4481e-07, 3.4527e-11, 2.1388e-10,
4.9739e-10, 1.9941e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(5.4500e-11, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.3836e-07, device='cuda:2', grad_fn=<SubBackward0>)}
tensor([9.9999e-01, 2.4824e-07, 1.8084e-07, 7.4775e-10, 2.2126e-11, 5.0572e-06,
7.4849e-11, 3.0373e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9999e-01, 2.4824e-07, 1.8084e-07, 7.4775e-10, 2.2126e-11, 5.0572e-06,
7.4849e-11, 3.0373e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.4901e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:22:41,873] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.51 | optimizer_gradients: 0.22 | optimizer_step: 0.30
[2024-10-24 10:22:41,873] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7109.21 | backward_microstep: 6828.19 | backward_inner_microstep: 6823.56 | backward_allreduce_microstep: 4.56 | step_microstep: 7.55
[2024-10-24 10:22:41,873] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7109.21 | backward: 6828.19 | backward_inner: 6823.58 | backward_allreduce: 4.54 | step: 7.57
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4734/4844 [19:41:25<26:10, 14.27s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Does the landscape show a cloudy blue sky?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Do the doors in the image open to a grassy area?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many sails does the sail boat have engaged?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([4, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Does the image have a horse with a red tassel on its head?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Do the doors in the image open to a grassy area?'], responses:['yes']
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
torch.Size([4, 3, 448, 448]) knan debug pixel values shape
question: ['Does the landscape show a cloudy blue sky?'], responses:['yes']
question: ['How many sails does the sail boat have engaged?'], responses:['1']
question: ['Does the image have a horse with a red tassel on its head?'], responses:['yes']
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
tensor([1.0000e+00, 1.7062e-09, 1.2626e-08, 2.4806e-09, 3.3492e-12, 1.6752e-11,
5.4468e-12, 1.3185e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.7062e-09, 1.2626e-08, 2.4806e-09, 3.3492e-12, 1.6752e-11,
5.4468e-12, 1.3185e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.2626e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.2626e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the writing in the image cursive?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404